Establish a “data first” mindset: how can AI drive change?

The continuous development and progress of AI technology has promoted technological innovation in some companies. Let us learn how to use AI to drive change.

The emergence of artificial intelligence has changed the pace of economic development in the world, and it is also a disruptive technological change for the development of enterprises. If enterprises want to develop, they must break through the current growth bottleneck. The first thing is to change cognition and establish a "data first" consciousness.

So what is the "data first" cognition? Simply put, it is the ability to "businessize data" and "dataize business". In other words, the "data first" consciousness includes the above two aspects. And the above two points are also the key implementation paths for enterprises to carry out digital transformation.

Data has become the fifth largest production factor after land, labor, capital, and technology. And no matter how artificial intelligence technology develops and evolves, its underlying logic is always data, followed by algorithms and computing power in the middle, and then various large models on top, which are applied to all walks of life and various scenarios. Therefore, the capture and effective use of data has also become the most critical prerequisite for driving AI transformation.

For enterprises, the construction of underlying data warehouses and the training of enterprise managers and relevant data experts to be able to transform business into data and data into business insights have become one of the core competitive advantages of enterprises.

The question is, what data will enterprises generate during the production and operation process?

In fact, enterprises generate a variety of data in their business activities, and these data are usually scattered in various corners and are easily overlooked. At the same time, the challenge facing enterprises today is how to fully collect and integrate the data scattered in various places to help improve the company's revenue and response efficiency. The overall enterprise data assets can be divided into the following categories:

01

Customer data: Customer data is divided into key customers and general customer data. Important customer companies can customize it according to actual conditions. For example, customers with annual revenue exceeding RMB 1 million are considered key customers. Customers with relatively large scale and annual turnover exceeding RMB 10 billion, although the sales contribution is currently relatively small (less than RMB 1 million), can also be defined as secondary key customers if there is a chance to expand business opportunities with this customer in the future.

Therefore, in the definition of key customers, we can divide them into four quadrants with sales revenue as the horizontal axis and development potential as the vertical axis. Those with high income and high development potential are key customers, those with low income and high development potential are also key customers, those with high income and low development potential are general maintained customers, and those with low income and low development potential are abandonable customers who only need the simplest follow-up.

So what is the use of this data? For different types of customers, the maintenance manpower and resources that enterprises need to invest are different, which means that the investment of resources must be strategically differentiated and carried out in a focused manner.

From an individualized perspective, customer data can cover many data dimensions, such as basic customer information, name, company name, industry, function in the company, contact information, etc. This basic information also becomes the basic information for companies to obtain customer leads in marketing activities, and can be used for future marketing activities such as customer activation, lead incubation and performance marketing conversion.

In addition to the basic information of customers, more in-depth data is needed, such as the behavior of individual customers, their interactions with the brand, the frequency of interactions with the brand, what products were purchased at what time and through what channels, the amount of a single order, and the influence of the customer in the industry. These key data help brands define the nature and characteristics of key and non-key customers on the one hand, so as to decide how to carry out purposeful, efficient and personalized marketing activities for customers in the future. The key word here is "personalization."

Today, AI technology plays a decisive role in analyzing and predicting personalized customer behavior. Before the advent of the AI ​​era, all this would have been impossible.

02

Marketing data: Marketing data can be divided into total data and individual data. Total data includes market research data and advertising and marketing data.

Research data includes the overall scale of the industry, market competition pattern, market share of competing products, strategic research of competitors, changes in overall consumer trends and behavioral research and products. These data can provide companies with market trend analysis and forecasts, helping them seize market opportunities and respond to market changes. This type of data is also an essential step for marketers to formulate marketing strategies before launching marketing activities.

Advertising and marketing data can be divided into various types of data generated by different advertising and marketing channels such as brand websites, social media, television media, outdoor media, e-commerce websites, etc., including impressions, clicks, click costs, conversion rates, retention rates and other common data used to analyze media placement or advertising and marketing effectiveness.

However, with the development of artificial intelligence technology, the value of this type of data is decreasing. Many advertising companies still use this data to prove that the services they provide are valuable and effective when reporting to the client, but in fact, it is becoming less and less convincing. The defect of this type of data is that in addition to drawing conclusions about "whether the marketing activities are good or not", it is difficult to generate real "data business" value, that is, what should be done next? Does the allocation of marketing resources need to be adjusted, and how to adjust it?

In comparison, what can generate more value is individual marketing data. For example, through a marketing campaign, you attract customers. What is the profile of high-conversion customers? What characteristics do they have? What is the value of this to marketing?

That is to predict the future. After defining this group of potential customers with strong intentions, can we use algorithms to make predictions about future potential customers and reach a wider range of potential customers with similar group characteristics, on the one hand to broaden the coverage of precise populations, and on the other hand to provide more personalized content and conversion methods? The key word here is still "personalization."

Personalized marketing data typically also includes comments and opinions posted by users on social media about products, what are the positive and negative information, and the rankings of corporate brands and key products on search engines. What is the value of this data to the brand? The key lies in the analysis of these "word-of-mouth" data. Companies can understand consumers' feedback on corporate brands and products, maintain a good reputation, and build consumer trust.

03

Other data assets of enterprises include supply chain data, production data, financial data, and human resources data. These data are generated in different stages of business activities and are under the jurisdiction of different functional departments. For large and medium-sized enterprises, establishing an integrated data warehouse at the bottom level is of utmost importance.

It is a very challenging task to fully integrate and utilize various types of data in the production and operation process of an enterprise. Whether from the level of digital infrastructure or the collaboration between personnel in different departments, the setting up of relevant data personnel positions has extremely high requirements. The key implementation path of the digital transformation of an enterprise is reflected in the construction of digital infrastructure and the ability to collect and utilize data.

Business datafication is the process of collecting and retaining various data generated by the company's business activities using digital tools. The next more critical step is to commercialize the data, which is the process of converting data into core business insights, that is, to make effective use of the data.

With the help of AI tools and human wisdom, data can be interpreted and converted into strategic marketing and business steps that are valuable for key indicators of enterprise development, such as sales growth, profit increase, and operational efficiency improvement. From the data, enterprises can determine what should be done, what should not be done, and where method innovation is necessary, thereby breaking down the data into strategic steps that can be implemented, realized, measurable, accurate and effective. This is also one of the most imaginative implementation scenarios of AI products in business growth.

But it should also be noted that with the emergence of the Sora video model, AI is developing rapidly. Sora can be regarded as another disruptive advancement in AI technology, but AI cannot replace many jobs that still require humans in the short term. The process of business dataization and data businessization still requires a lot of human intervention. Therefore, Copilot's working mode will be a way of working that will exist for a long time to come. This also means that both managers and professionals need to start learning to use various AI tools to help them manage their daily work.

In the next five years, the development and application of AI may replace many basic positions in enterprises and eliminate those who have not learned to use AI tools, or are unwilling or have not tried to integrate AI tools into their daily work. This is not an exaggeration.

Keeping pace with the times, keeping pace with the development of AI, actively embracing and absorbing the latest technologies, staying rational and objective, and making use of AI and playing a positive role will become a compulsory course for every working person.

Author: Zhu Jingyu WeChat Official Account: Jade Talks About Digital Marketing